一种基于社会化信任的改进的协同过滤推荐模型和方法

An Improved Collaborative Filtering Recommendation Model and Method Based on Social Trust

社会化商务环境中的信任关系深刻地影响着消费者的购买行为与决策,成为了支撑网络商务活动开展的重要因素。基于用户历史评价数据,协同过滤推荐算法通常面临着数据稀疏的问题,即评分数据过于稀疏导致推荐质量下降。为了解决这一问题,结合辅助数据成为一种必然的趋势。因此,随着社交媒体的发展,基于信任关系的社会化推荐算法被证明为一种有效的解决方法。然而,目前大部分算法直接利用社交网络的二值信任关系来提高推荐质量,没有考虑用户对每个好友信任强度的差异。为了提高社会化推荐算法的准确性,本文以社交数据为基础,计算用户个人可信度评分和互鉴可信度评分,并基于可信评分对社交关注矩阵进行可信量化,以及基于评分矩阵预填充的思想来缓解数据稀疏性问题。基于大众点评真实数据集的实验与分析结果表明,本文提出的新的协同过滤推荐模型与算法,进一步提高了推荐精度。

Trust relationship in the socialbusiness environment has a profound impact on consumers'purchase behavior anddecision-making,and has become an important factor to support the developmentof online business activities.Collaborative filtering recommendation algorithmbased on user history evaluation data usually faces the problem of data sparseness;that is,the sparse rating data leads to the decline of recommendation quality.In order to solve this problem,the combination of auxiliary data has become aninevitable trend.Therefore,with the development of social media,trust-basedsocial recommendation algorithm has been proved to be an effective solution.However,most of the current algorithms directly use the binary trustrelationship of the social network to improve the recommendation quality,without considering the difference in the trust strength of the user for eachfriend.In order to improve the accuracy of social recommendation algorithm,this paper calculates personal reliability and mutual identify reliabilitybased on social data,and quantifies the social attention matrix based on mutualidentify reliability and alleviates the data sparsity problem based on the ideaof score matrix pre-filling.The experiment and analysis results based on thereal data set of public comments show that the new collaborative filteringrecommendation model and algorithm proposed in this paper further improve therecommendation accuracy.